Document Type

Technical Report

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Publication Title

LUC CS - Technical Reports


Anti-Asian prejudice increased during the COVID-19 pandemic, evidenced by a rise in physical attacks on individuals of Asian descent. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media platforms such as Twitter. In the present study, we investigated temporal and geographic patterns in the prevalence of social media content relevant to anti-Asian prejudice within the U.S. and worldwide. Specifically, we used the Twitter Data Collection API to query over 13 million tweets posted during the first 15 months of the pandemic (i.e., from January 30, 2020, to April 30, 2021), for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. Results of a range of exploratory and descriptive analyses offer novel insights. For instance, in the U.S., the prevalence of anti-Asian and counter-hate messages fluctuated over time in patterns that largely mirrored salient events relevant to COVID-19 (e.g., political tweets, highly-visible hate crimes targeting Asians). Geographic differences in the frequency of negative and positive keywords also emerged, shedding light on the regions within the U.S. and the countries worldwide in which negative and positive messages were most frequent. Additional analyses revealed informative patterns in the prevalence of original tweets versus retweets, the co-occurrence of negative and positive content within a tweet, and fluctuations in content in relation to the number of new COVID-19 cases and reported COVID-related deaths. Together, these findings underscore the value of research examining trends in social media messages of hate and counter-hate during the COVID-19 pandemic.


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Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.